logo
ResearchBunny Logo
Overcoming resistance to BRAF V600E inhibition in melanoma by deciphering and targeting personalized protein network alterations

Medicine and Health

Overcoming resistance to BRAF V600E inhibition in melanoma by deciphering and targeting personalized protein network alterations

S. Vasudevan, E. Flashner-abramson, et al.

This groundbreaking study by S. Vasudevan, E. Flashner-Abramson, and colleagues unveils a personalized approach to combating BRAF V600E melanoma relapses. By utilizing high-resolution signaling signatures to craft individualized drug combinations, the research demonstrates remarkable efficacy compared to standard therapies, offering new hope for patients facing drug resistance.

00:00
00:00
Playback language: English
Abstract
BRAF V600E melanoma patients, despite initial response to anti-BRAF V600E therapy, often relapse due to drug resistance. This study designs individualized melanoma combination treatments based on personalized network alterations using an information-theoretic approach. High-resolution patient-specific altered signaling signatures are computed, consisting of co-expressed subnetworks. Smart, personalized drug combinations (often FDA-approved) are designed based on these signatures, showing superior efficacy in vitro and in vivo compared to standard therapies. This approach is highly selective, and broadly applicable for designing patient-specific anti-melanoma drug combinations.
Publisher
npj Precision Oncology
Published On
Jun 10, 2021
Authors
S. Vasudevan, E. Flashner-Abramson, Heba Alkhaiti, Sangita Roy Chowdhury, I. A. Adejumobi, D. Vlienski, S. Stefansky, A. M. Rubinstein, N. Kravchenko-Balasha
Tags
BRAF V600E
melanoma
drug resistance
personalized medicine
combination therapy
signaling signatures
FDA-approved drugs
Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny